Financial Statement Analysis at Booth: reading what companies are actually saying
Abbi Smith's course taught me to read financial statements the way a detective reads a crime scene — not for what is obvious, but for what the choices reveal about the people who made them.
I came into Financial Statement Analysis with Abbi Smith thinking I already understood the basics. I had seen income statements and balance sheets before. I knew the vocabulary. What I did not have was a serious analytical framework for what those statements were actually communicating — and what they were strategically not communicating.
The course fixed that.
The course is about judgment, not mechanics
Smith does not spend much time on definitions. The course assumes you can read a financial statement. What it teaches is how to interrogate one.
The central skill is identifying where management has discretion — in revenue recognition timing, depreciation choices, capitalization versus expensing decisions, goodwill treatment, lease classification — and then developing a view on what those choices tell you about how the business actually performed versus how it was presented.
That is not a cynical framing. Companies make these choices for legitimate reasons. But when you are doing serious analysis, you need to know where the discretion was exercised and whether the choices are consistent with the story management is telling.
The ability to hold both things at once — the reported numbers and the underlying economic reality they may or may not be tracking — is what separates useful financial analysis from number reporting.
What changes when you learn this
Before this course, I would read a P&L and evaluate it primarily by asking: are these numbers good or bad? After the course, I ask a different set of questions first.
What accounting choices are embedded in these results? Are revenue recognition policies aggressive or conservative relative to peers? Is the company capitalizing expenses that competitors expense — which inflates current earnings at the cost of future amortization charges? What does the cash flow statement say about whether the earnings story is real?
Once you start asking those questions, financial performance becomes much harder to evaluate quickly and much more informative when you do the work.
This matters well beyond investing or finance. Product leaders, operators, and founders who can read financial statements rigorously have a substantial advantage in understanding their own business and the dynamics of the markets they operate in. Booth made this non-negotiable in a way that I think was exactly right.
The connection to AI and information systems
I took FSA in the same quarter as AI and Financial Information, and the two courses created an interesting cross-current.
The AI and Financial Information course pushed on what it means to build systems that help people make better decisions with imperfect information. FSA pushed on a specific domain where information imperfection is built into the structure — where the same economic event can be accounted for in multiple ways, each one technically correct.
The overlap is that both courses are ultimately about the same thing: how do you reason under conditions where the data you are working with is shaped by the choices of the people who produced it? That is true of financial statements. It is also true of every dataset, every product metric, every LLM output.
FSA gave me a more grounded model of what it means to take that seriously in a real domain. It is one of the more practically useful courses I have taken at Booth.